agentcard

redeem_rewards

Redeem tokenback: the tokens' cash value (1 token = 1¢) lands on the user's rewards card as spending power. The rewards card is permanent and locked to AI-lab merchants (OpenAI, Anthropic, Gemini) — created on first redemption, topped up after. Check get_rewards first for the balance and the minimum. Ask the user before redeeming.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "tokens": "<tokens>"
    },
    "name": "redeem_rewards"
  }
}

Input Schema

Name	Required	Description	Default
`tokens`	Yes	How many tokens to redeem (1 token = 1 cent, so 500 tokens = $5.00 of wallet credit).

Output Schema

Name	Required	Description
`status`	No	"redeemed" on success; an error discriminator otherwise (e.g. "below_minimum", "insufficient_tokens", "redemption_in_progress").
`message`	Yes	Human-readable result.
`amountCents`	No	Wallet credit in cents. Present when status is "redeemed".
`redemptionId`	No	The redemption ID. Present when status is "redeemed".
`deliveredCardId`	No	Rewards card the value landed on, when delivery completed inline. Absent = the value sits as wallet credit (it reaches the rewards card within a few minutes when delivery is enabled).
`deliveredCardLast4`	No	Last 4 digits of the rewards card, when delivered inline.

Schema Changelog

Changes observed during successful MCP inspections.

First observedJul 28, 2026

TDQS

A4.3/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses important behavioral details beyond annotations: tokens convert at a fixed rate, the rewards card is permanent, locked to specific merchants, created on first redemption, and topped up afterward. Since annotations only indicate non-read-only but don't specify effects, these details add significant transparency. It could further mention irreversibility but is not required for basic use.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences, front-loaded with the core action and effect, then a key restriction, then brief usage guidance. Every sentence adds essential information with no redundancy or fluff, making it highly concise and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a one-parameter tool with an output schema, the description covers all essential aspects: what happens, conversion rate, card behavior, merchant restriction, prerequisite check, and user consent. It doesn't explain every edge case but points to get_rewards for balance/minimum, which is sufficient for correct invocation. This is complete for the tool's simplicity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema for the single parameter 'tokens' already provides a full description including the conversion rate and an example, giving 100% coverage. The tool description repeats the rate but adds no new parameter-level constraints or format details. Therefore, it meets the baseline for schema-covered parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Redeem tokenback' with a specific conversion rate and effect on the rewards card. It distinguishes itself from sibling tools like redeem_code and withdraw by describing the unique reward card mechanism and AI-lab merchant restrictions. The verb 'redeem' plus the specific resource 'tokenback' makes the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear usage context: 'Check get_rewards first for the balance and the minimum' and 'Ask the user before redeeming.' This gives a prerequisite and consent requirement, but it does not explicitly name alternatives or when-not-to-use conditions, so it stops short of a 5. The guidance is still effective for the agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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